Unified ML Pipeline with Data Adapters Across Dev and Production
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Solution Overview
Problem
Existing machine learning (ML) pipelines face challenges in managing infrastructure and data format differences between development and production environments, leading to disjointed computing systems and difficulties in updating models effectively.
Innovation Solution
A cloud computing system with a unified ML pipeline that includes training and production data adapters, loggers, and a machine learning pipeline, which automatically adjusts data formats and determines the appropriate environment for training or production, facilitating seamless integration and synchronization of data across environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate ML pipelines are established for development and production environments, then each environment can be optimized independently, but the system complexity increases and model updates become difficult
Solution Approach 1:
The patent merges development and production environments into a single unified ML pipeline. The pipeline receives data from multiple sources including development databases and production databases, processes them through common processing steps, and outputs to both development and production destinations. This consolidation reduces system complexity while maintaining the ability to optimize for different environments through configurable data sources and destinations.
Solution Approach 2:
The unified ML pipeline is designed to serve multiple functions simultaneously. It can process development data, production data, or both together; it can output to development databases, production databases, or both; and it maintains compatibility with different data formats and sources. This multi-functionality allows a single pipeline to replace multiple environment-specific pipelines.
2Ease of operation
If data formats are standardized across environments, then integration becomes easier, but flexibility in handling environment-specific data formats is reduced
Solution Approach 1:
The unified ML pipeline acts as an intermediary between diverse data sources and destinations. It includes configurable data input modules that can handle different data formats from development databases, production databases, or other sources. The pipeline transforms these varied inputs into a standardized internal format for processing, then outputs results in appropriate formats for different destinations, thus mediating between format diversity and processing consistency.
Solution Approach 2:
The pipeline employs configurable parameters to adapt to different data formats and processing requirements. Data source configurations, data destination configurations, and processing parameters can be adjusted based on whether the pipeline is processing development data, production data, or both. This parameter-based flexibility allows the same pipeline structure to handle varying data formats without requiring separate pipelines.
3Reliability
If manual intervention is required for model training and deployment, then control and monitoring are improved, but processing time and operational complexity increase
Solution Approach 1:
The unified ML pipeline is designed to operate autonomously with minimal manual intervention. It automatically retrieves data from configured sources, processes the data through defined processing steps, trains models, and deploys them to appropriate destinations. The pipeline includes automatic error handling, logging, and notification mechanisms that provide monitoring and control capabilities without requiring constant human oversight, thus reducing processing time while maintaining reliability.
Data Source
AI summary
Systems and methods are provided for a machine learning (ML) pipeline with a unified framework. A training data adapter receives training data in a training data format, processes the training data to match a pipeline data format of a ML pipeline, and transmits reformatted training data to the ML pipeline. A production data adapter receives production data in a production data format, processes the production data to match the pipeline data format, and transmits the reformatted production data to the ML pipeline. The ML pipeline receives and processes the reformatted training data to train a ML model in the ML pipeline, and further receives and processes the reformatted production data using the ML model to generate production artifacts.